Data Visualization

Understanding Machine Learning Through Visualizations with Benjamin Bengfort and Rebecca Bilbro - Episode 166

Summary

Machine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Rebecca Bilbro and Benjamin Bengfort about Yellowbrick, a scikit extension to use visualizations for assisting with model selection in your data science projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you describe the use case for Yellowbrick and how the project got started?
  • What is involved in visualizing scikit-learn models?
    • What kinds of information do the visualizations convey?
    • How do they aid in understanding what is happening in the models?
  • How much direction does yellowbrick provide in terms of knowing which visualizations will be helpful in various circumstances?
  • What does the workflow look like for someone using Yellowbrick while iterating on a data science project?
  • What are some of the common points of confusion that your students encounter when learning data science and how has yellowbrick assisted in achieving understanding?
  • How is Yellowbrick iplemented and how has the design changed over the lifetime of the project?
  • What would be required to integrate with other visualization libraries and what benefits (if any) might that provide?
    • What about other ML frameworks?
  • What are some of the most challenging or unexpected aspects of building and maintaining Yellowbrick?
  • What are the limitations or edge cases for yellowbrick?
  • What do you have planned for the future of yellowbrick?
  • Beyond visualization, what are some of the other areas that you would like to see innovation in how data science is taught and/or conducted to make it more accessible?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi - Episode 157

Summary

One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • Your host as usual is Tobias Macey and today I’m interviewing Olivier Corradi about Electricity Map and using Python to analyze data of global power generation

Interview

  • Introductions
  • How did you get introduced to Python?
  • What was your motivation for creating Electricity Map?
    • How can an average person use or benefit from the information that is available in the map?
  • What sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?
    • Is there any standard format in which this data is produced?
    • What are the biggest difficulties associated with collecting and consuming this data?
    • How much confidence do you have in the accuracy of the data sources?
    • Is there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?
  • Can you describe the architecture of the system and how it has evolved?
  • What are some of the most interesting uses of the data in your database and API that you are aware of?
    • How do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?
  • How have you built a community around the project?
    • How has the community helped in building and growing Electricity Map?
  • What are some of the most unexpected things that you have learned in the process of building Electricity Map?
  • What are your plans for the future of Electricity Map?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Orange: Visual Data Mining Toolkit with Janez Demšar and Blaž Zupan - Episode 142

Summary

Data mining and visualization are important skills to have in the modern era, regardless of your job responsibilities. In order to make it easier to learn and use these techniques and technologies Blaž Zupan and Janez Demšar, along with many others, have created Orange. In this episode they explain how they built a visual programming interface for creating data analysis and machine learning workflows to simplify the work of gaining insights from the myriad data sources that are available. They discuss the history of the project, how it is built, the challenges that they have faced, and how they plan on growing and improving it in the future.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Blaž Zupan and Janez Demsar about Orange, a toolbox for interactive machine learning and data visualization in Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Orange and what was your motivation for building it?
  • Who is the target audience for this project?
  • How is the graphical interface implemented and what kinds of workflows can be implemented with the visual components?
  • What are some of the most notable or interesting widgets that are available in the catalog?
  • What are the limitations of the graphical interface and what options do user have when they reach those limits?
  • What have been some of the most challenging aspects of building and maintaining Orange?
  • What are some of the most common difficulties that you have seen when users are just getting started with data analysis and machine learning, and how does Orange help overcome those gaps in understanding?
  • What are some of the most interesting or innovative uses of Orange that you are aware of?
  • What are some of the projects or technologies that you consider to be your competition?
  • Under what circumstances would you advise against using Orange?
  • What are some widgets that you would like to see in future versions?
  • What do you have planned for future releases of Orange?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Jake Vanderplas: Data Science For Academic Research - Episode 140

Summary

Jake Vanderplas is an astronomer by training and a prolific contributor to the Python data science ecosystem. His current role is using Python to teach principles of data analysis and data visualization to students and researchers at the University of Washington. In this episode he discusses how he got started with Python, the challenges of teaching best practices for software engineering and reproducible analysis, and how easy to use tools for data visualization can help democratize access to, and understanding of, data.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Jake Vanderplas about data science best practices, and applying them to academic sciences

Interview

  • Introductions
  • How did you get introduced to Python?
  • How has your astronomy background informed and influenced your current work?
  • In your work at the University of Washington, what are some of the most common difficulties that students face when learning data science?
    • How does that list differ for professional scientists who are learning how to apply data science to their work?
  • Where is the tooling still lacking in terms of enabling consistent and repeatable workflows?
  • One of the projects that you are spending time on now is Altair, which is a library for generating visualizations from Pandas dataframes. How does that work factor into your teaching?
  • What are some of the most novel applications of data science that you have been involved with?
  • What are some of the trends in data analysis that you are most excited for?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Graphite Metrics Stack with Jason Dixon and Dan Cech - Episode 136

Summary

Do you know what is happening in your production systems right now? If you have a comprehensive metrics platform then the answer is yes. If your answer is no, then this episode is for you. Jason Dixon and Dan Cech, core maintainers of the Graphite project, talk about how graphite is architected to capture your time series data and give you the ability to use it for answering questions. They cover the challenges that have been faced in evolving the project, the strengths that have let it stand the tests of time, and the features that will be coming in future releases.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Now is a good time to start planning your conference schedule for 2018. To help you out with that, guest Jason Dixon is offering a $100 discount for Monitorama in Portland, OR on June 4th – 6th and guest Dan Cech is offering a €50 discount to Grafanacon in Amsterdam, Netherlands March 1st and 2nd. There is also still time to get your tickets to PyCascades in Vancouver, BC Canada January 22nd and 23rd. All of the details are in the show notes
  • Your host as usual is Tobias Macey and today I’m interviewing Jason Dixon and Dan Cech about Graphite

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Graphite and how did you each get involved in the project?
  • Why should developers be thinking about collecting and reporting on metrics from their software and systems?
  • How do you think the Graphite project has contributed to or influenced the overall state of the art in systems monitoring?
  • There are a number of different projects that comprise a fully working Graphite deployment. Can you list each of them and describe how they fit together?
  • What are some of the early design choices that have proven to be problematic while trying to evolve the project?
  • What are some of the challenges that you have been faced with while maintaining and improving the various Graphite projects?
  • What will be involved in porting Graphite to run on Python 3?
  • If you were to start the project over would you still use Python?
  • What are the options for scaling Graphite and making it highly available?
  • Given the level of importance to a companies visibility into their systems, what development practices do you use to ensure that Graphite can operate reliably and fail gracefully?
  • What are some of the biggest competitors to Graphite?
  • When is Graphite not the right choice for tracking your system metrics?
  • What are some of the most interesting or unusual uses of Graphite that you are aware of?
  • What are some of the new features and enhancements that are planned for the future of Graphite?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

MetPy: Taming The Weather With Python - Episode 100

Summary

What’s the weather tomorrow? That’s the question that meteorologists are always trying to get better at answering. This week the developers of MetPy discuss how their project is used in that quest and the challenges that are inherent in atmospheric and weather research. It is a fascinating look at dealing with uncertainty and using messy, multidimensional data to model a massively complex system.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host as usual is Tobias Macey and today I’m interviewing Ryan May, Sean Arms, and John Leeman about MetPy, a collection of tools and notebooks for analyzing meteorological data in Python.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is MetPy and what is the problem that prompted you to create it?
  • Can you explain the problem domain for Meteorology and how it compares to other domains such as the physical sciences?
  • How do you deal with the inherent uncertainty of atmospheric and weather data?
  • What are some of the data sources and data formats that a meteorologist works with?
  • To what degree is machine learning or artificial intelligence employed when modelling climate and local weather patterns?
  • The MetPy documentation has a number of examples of how to use the library and a number of them produce some fairly complex plots and graphs. How prevalent is the need to interact with meteorological data visually to properly understand what it is trying to tell you?
  • I read through your developer guide and watched your SciPy talk about development automation in MetPy. My understanding is that individuals with a pure science background tend to eschew formal code styles and software engineering practices so I’m curious what your experience has been when interacting with your user community.
  • What are some of the interesting innovations in weather science that you are looking forward to?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

VPython with Ruth Chabay and Bruce Sherwood - Episode 49

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Summary

Wouldn’t it be nice to be able to generate interactive 3D visualizations of physical systems in a declarative manner with Python? In this episode we spoke with Ruth Chabay and Bruce Sherwood about the VPython project which does just that. They tell us about how the use VPython in their classrooms, how the project got started, and the work they have done to bring it into the browser.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Ruth Chabay and Bruce Sherwood about their work on VPython

Interview

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is VPython and how did it get started? – Tobias
  • What problems inspired you to create VPython? – Chris
  • How do you design an API that allows for such powerful 3D visualization while still making it accessible to students who are focusing on learning new concepts in mathematics and physics so that they don’t get overwhelmed by the tool? – Tobias
  • I know many schools have embraced the open curriculum idea, have any of your physics courses using VPython been made available to the non matriculating public? – Chris
  • How does VPython perform its rendering? If you were to reimplement it would you do anything differently? – Tobias
  • One of the remarkable points about VPython is its ability to execute the simulations in a browser environment. Can you explain the technologies involved to make that work? – Tobias
  • Given the real-time rendering capabilities in VPython I’m sure that performance is a core concern for the project. What are some of the methods that are used to ensure an appropriate level of speed and does the cross-platform nature of the package pose any additional challenges? – Tobias
  • How does collision detection work in VPython, and does it handle more complex assemblies of component objects? – Chris
  • Can you talk a little bit about VPython’s design, and perhaps walk us through how a simple scene is rendered, say the results of the sphere() call? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

PyData London with Ian Ozsvald and Emlyn Clay - Episode 48

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Summary

Ian Ozsvald and Emlyn Clay are co-chairs of the London chapter of the PyData organization. In this episode we talked to them about their experience managing the PyData conference and meetup, what the PyData organization does, and their thoughts on using Python for data analytics in their work.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Ian Ozsvald and Emlyn Clay about their work with PyData London, a group within the PyData organization. PyData London represents the largest Python group in London at ~2850 members, they hold regular monthly meetups for ~200 members at AHL near Bank and a yearly conference for around ~300 members. Last year, they and their sponsors raised over £26,000 to sponsor the development of core numerical libraries in Python.
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On Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

Interview

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is the PyData organization, how does PyData London fit into it and what is your relationship with it? – Tobias
  • In what ways does a PyData conference differ from a PyCon? – Tobias
  • Does PyData do anything in particular to encourage users from disciplines that might not be aware of how much our community has to offer to choose the Python suite of data analysis tools? – Chris
  • You have both spent a good portion of your careers using Python for working with and analyzing data from various domains. How has that experience evolved over the past several years as newer tools have become available? – Tobias
  • For someone who is just getting started in the data analytics space, what advice can you give? – Tobias
  • How can conferences like PyData help strengthen the bonds and synergies between the Python software community and the sciences? – Chris
  • There are a number of different subtopics within the blanket categorization of data science. Is it difficult to balance the subject matter in PyData conferences and meetups to keep members of the audience from being alienated? – Tobias
  • Data science is a young field and we’ve yet to see lots of examples of the successful use of data. How are London-based companies using data with Python? – Ian
  • Is there a Python data science library you think needs a little love? – Emlyn

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Yves Hilpisch on Quantitative Finance - Episode 39

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Summary

Yves Hilpisch is a founder of The Python Quants, a consultancy that offers services in the space of quantitative financial analysis. In addition, they have created open source libraries to help with that analysis. In this episode we spoke with him about what quantitative finance is, how Python is used in that domain, and what kinds of knowledge are necessary to do these kinds of analysis.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus to $4,000.
  • We are recording today on December 30th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Yves Hilpisch about Quantitative Finance
Hired LogoOn Hired software engineers & designers can get 5+ interview requests in a week and each offer has salary and equity upfront. With full time and contract opportunities available, users can view the offers and accept or reject them before talking to any company. Work with over 2,500 companies from startups to large public companies hailing from 12 major tech hubs in North America and Europe. Hired is totally free for users and If you get a job you’ll get a $2,000 “thank you” bonus. If you use our special link to signup, then that bonus will double to $4,000 when you accept a job. If you’re not looking for a job but know someone who is, you can refer them to Hired and get a $1,337 bonus when they accept a job.

Interview with Yves Hilpisch

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you explain what Quantitative Finance is? – Tobias
  • How common is it for Python to be used in an investment bank or hedge fund? – Tobias
  • What factors contribute to the choice of whether or not to use Python in a Quantitative Finance role? – Tobias
  • Are there any performance bottle necks or other considerations inherent in using Python for quantitative finance? – Chris
  • What kind of background is necessary for getting started in Quantitative Finance? – Tobias
  • What kinds of libraries or algorithms in Python are useful for the day-to-day work of a quant? – Tobias
  • Is Python actually used to enact the trades? What protocols, APis, and libraries are used in this process? – Chris
  • Could you please walk us through how a simple analysis using DXAnalytics might work? – Chris
  • You work for a company called ‘The Python Quants‘. What kinds of services do you provide and what kinds of organizations typically hire you? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Scott Sanderson on Algorithmic Trading - Episode 38

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Summary

Because of its easy learning curve and broad extensibility Python has found its way into the realm of algorithmic trading at Quantopian. In this episode we spoke with Scott Sanderson about what algorithmic trading is, how it differs from high frequency trading, and how they leverage Python for empowering everyone to try their hand at it.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • We are recording today on December 16th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Scott Sanderson on Algorithmic Trading

Interview with Scott Sanderson

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you explain what algorithmic trading is and how it differs from high frequency trading? – Tobias
  • What kinds of algorithms and libraries are commonly leveraged for algorithmic trading? – Tobias
  • Quantopian aims to make algorithmic trading accessible to everyone. What do people need to know in order to get started? Is it necessary to have a background in mathematics or data analysis? – Tobias
  • Does the Quantopian platform build in any safe guards to prevent user’s algorithms from spiraling out of control and creating or contributing to a market crash? – Chris
  • How is Python used within Quantopian and when do you leverage other languages? – Tobias
  • What Pypi packages does Quantopian leverage in its platform? – Chris
  • How do the financial returns compare between algorithmic vs human trading on the stock market? – Tobias
  • Can you speak about any trends you see in the trading algorithms people are creating for the Quantopian platform? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA